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Qwen3.5-4B GUI Grounding — v1 (SFT LoRA)

LoRA adapter for Qwen3.5-4B fine-tuned on GUI grounding: given a screenshot and a natural language instruction, predict the (x, y) click coordinate of the target UI element.

Results — ScreenSpot-V2

Split	Accuracy
Overall	92.5%

Training Data

~23.5K samples from 3 GUI grounding datasets covering desktop, web, and mobile platforms.

Output Format

<|box_start|>(x,y)<|box_end|>

Coordinates are in [0, 1000] normalized space. To convert to pixel coordinates:

pixel_x = x / 1000 * image_width
pixel_y = y / 1000 * image_height

Usage

Requires transformers>=5.2.0 and peft.

from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
from peft import PeftModel
import torch

base = Qwen3_5ForConditionalGeneration.from_pretrained("Qwen/Qwen3.5-4B", torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base, "dabism23/qwen35-gui-grounding")
processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-4B")

Access

Model weights are gated. Request access to download. Training configuration details are included with the model files.

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qwen35-gui-grounding